Methods for automated semantic labeling of data are an indispensable basis for increasing the usability of data. On the one hand, they contribute to the homogenization of the annotations and thus to the increase in quality; on the other hand, they reduce the modeling effort, provided that the quality of the used methodology is sufficient. In the past, research has focused primarily on data- and label-based methods. Another approach that has received recent attention is the incorporation of textual data documentations to support the automatic mapping of datasets to a knowledge graph. However, upon deeper analysis, our recent approach called DocSemMap gives away potential in a number of places. In this paper, we extend the current state of the art approach by uncovering existing shortcomings and presenting our own improvements. Using a sequence-to-sequence model (Seq2Seq), we exploit the context of datasets. An additional introduced classifier provides the linkage of documentation and labels for prediction. Our extended approach achieves a sustainable improvement in comparison to the reference approach.

In recent years, Knowledge Graphs and Ontology-based Data Management have proven to be particularly effective in the efficient management and consolidation of heterogeneous data sources. In this context, semantic modeling has proven to be a useful approach for creating semantic data annotations. However, automatically generated semantic models usually need to be revised by a domain expert, who is often not familiar with semantic technologies. For addressing this issue, we propose the PLASMA semantic modeling tool, which aims at enabling domain experts to build semantic models from scratch or refine models created by automatic algorithms. We demonstrate the use of the tool and its user interface in two different semantic data management use cases for integrating smart city data in a public funded project, called City Dataspace, and for creating semantic models in an industrial use case at Siemens AG.

Today, smart city applications are largely based on data collected from different stakeholders. This presupposes that the required data sources are publicly available. While open data platforms already provide a number of urban data sources, enterprises and citizens have few opportunities to make their data available. To complicate things further, if the data is published, the processing of this data is already extremely time-consuming today, as the data sources are heterogeneous and the corresponding homogenization has to be carried out by the data consumers themselves. In this paper, we present a data marketplace that enables different stakeholders (public institutions, enterprises, citizens) to easily provide data that can especially contribute to the further realization of smart cities. This marketplace is based on the principles of semantic data management, i.e., data providers annotate their added data with semantic models. With the help of these models, the data sources can be found and understood by data consumers and finally homogenized in a way that is suitable for their application.

The potential for mHealth solutions is steadily increasing due to an enormous growth in the area of mobile networks and the mobile Internet. However, not only the general connection is becoming faster and more stable, but also the mobile devices themselves are becoming even more advanced. Nowadays, these devices are able to acquire physiological data and transfer them to e.g. a physician or technician to be analyzed before an actual appointment. Using these technological advantages, more and more evidence could be used for diagnosis and treatment. Instead, long preparation and delay are part of everyday practice nowadays and information and data acquisition take up much time before diagnosis.This paper describes a general concept for a centralized screening / pre-diagnosis system for mobile sleep laboratories. The system is designed to have as little influence as possible on the usual sleep environment, but still allows a medically usable recording of sleep activities and health parameters. Furthermore, the concept covers the access possibilities of the attending physician as well as the back-flow of a final diagnosis. Finally, we report on the resulting challenges of such systems with respect to privacy.

Gaze tracking is a common technique to study user interaction but is also increasingly used as input modality. In this regard, computer vision based systems provide a promising low-cost realization of gaze tracking on mobile devices. This paper complements related work focusing on algorithmic designs by conducting two users studies aiming to i) independently evaluate EyeTab as promising gaze tracking approach and ii) by providing the first independent use case driven evaluation of its applicability in mobile scenarios. Our evaluation elucidates the current state of mobile computer vision based gaze tracking and aims to pave the way for improved algorithms. In this regard, we aim to further foster the development by releasing our source data as reference database open to the public.